L10-Interactivity

Adding interactivity

This document demonstrates how to use the leaflet package to create interactive web maps and use animated transitions to enhance visual storytelling.

# Function to quietly install and load packages
quiet_library <- function(pkg) {
  suppressMessages(suppressWarnings({
    if (!requireNamespace(pkg, quietly = TRUE)) {
      install.packages(pkg, quiet = TRUE) # Install without messages
    }
    library(pkg, character.only = TRUE) # Load silently
  }))
}

quiet_library("ggplot2")
quiet_library("sf")               # For handling spatial data
quiet_library("ggspatial")        # Facilitates adding map elements to ggplot2
quiet_library("rnaturalearth")    # Provides world map data
quiet_library("rnaturalearthdata") 
quiet_library("geojsonio")        # For reading geo json data
quiet_library("leaflet")          # For interactivity in maps
quiet_library("magick")          # For swipe
quiet_library("tidyverse")
quiet_library("viridisLite")  # for consistent viridis palette
quiet_library("geosphere")      # For adding a route

mydata <- file.path("C:","Datasets")

Base Map

# setup the data for Ireland
ireland <-  rnaturalearth::ne_countries(scale = "medium", country = "Ireland", returnclass = "sf")

# Setup dataframe with longitude and latitude of major cities
cities <- data.frame(
  name = c("Dublin", "Cork", "Limerick", "Galway"),
  lon = c(-6.2603, -8.472, -8.6305, -9.0568),
  lat = c(53.3498, 51.8985, 52.6680, 53.2707)
)

# Using the shape data downloaded from 
# Replace this with your actual file path
json_file <- file.path(mydata, "gadm41_IRL_1.json")
                       
# Read the GeoJSON file into an sf object
countyshapes <- sf::st_read(json_file)

Reading layer `gadm41_IRL_1' from data source `C:\datasets\gadm41_IRL_1.json' using driver `GeoJSON'
Simple feature collection with 26 features and 11 fields
Geometry type: MULTIPOLYGON
Dimension:     XY
Bounding box:  xmin: -10.6628 ymin: 51.4199 xmax: -5.9945 ymax: 55.4351
Geodetic CRS:  WGS 84

# Replace NAME_1 with "Cork" where VARNAME_1 is "Corcaigh"
# For some reason some Cork data is NA for NAME_1 but has the Irish equivalen
countyshapes$NAME_1[countyshapes$VARNAME_1 == "Corcaigh"] <- "Cork"



# Read in a file from CSO data of median income per electoral region
incomeloc <-file.path(mydata,"MedianIncomeIreland.csv" )
countyincome <-read.csv(incomeloc) 

# Using a regular expression, extract county name after "Co." or Ending with Dublin, Cork City or Tipperary and add to the same dataset as column county
countyincome$county <- stringr::str_extract(countyincome$Electoral.Division, 
                                      "(?<=Co\\. )\\w+|Dublin$|Cork City$|Tipperary$")

# Regular Expression explanation:
# (?<=Co\. )\w+ - This is a lookbehind (?<=...), which ensures that the match only occurs if it is preceded by "Co. " (e.g., "Co. Mayo").
# \w+ matches one or more word characters (letters, numbers, or underscores), so it captures county names like "Mayo", "Galway", etc., after "Co. ".
# The | means OR.
# Dublin$ matches "Dublin" only if it appears at the end of the string ($ means end of the string).
# Cork City$ Matches "Cork City" only if it appears at the end of the string.
#  Tipperary$ Matches "Tipperary" only if it appears at the end of the string.

# Replace Cork City by Cork
countyincome <- countyincome %>%
  mutate(county = case_when(
    county == "Cork City" ~ "Cork",
    TRUE ~ county
  ))
# Rename column "VALUE" to "avg_income"
countyincome <- countyincome %>%
  rename(mdn_income = VALUE)
countyincome <- na.omit(countyincome)

# Summarize avg_income by county_name, omitting nas
summary_df <- na.omit(countyincome) %>%
  group_by(county) %>%
  summarize(mdn_income = median(mdn_income, na.rm = TRUE))


# Merge income data with spatial county boundaries
counties_income <- countyshapes %>%
  inner_join(summary_df, by = c("NAME_1" = "county")) %>% # Ensure column names match
  st_as_sf()  # Ensure it remains a spatial object with geometry
# Define a new projection (Irish Traverse Mercator)
crs_itransm <- 2157

# Reproject all spatial data
ireland_proj <- sf::st_transform(ireland, crs_itransm)
counties_proj <- sf::st_transform(countyshapes, crs_itransm)
counties_income_proj <- sf::st_transform(counties_income, crs_itransm)

# Convert city coordinates to projected CRS
cities_proj <- cities %>%
  sf::st_as_sf(coords = c("lon", "lat"), crs = 2157) %>% 
  sf::st_transform(crs_itransm) %>% 
  sf::st_coordinates() %>%
  as.data.frame() %>%
  mutate(name = cities$name) 

# Plot using the new projection
map1 <- ggplot2::ggplot() +
  geom_sf(data = ireland_proj, fill = "lightgreen", color = "darkblue") +
# County shapes
  geom_sf(data = counties_proj, fill = NA, color = "red", size = 0.5) +
# Cities
  geom_point(data = cities_proj, aes(x = X, y = Y), color = "red", size = 3) +
  geom_text(data = cities_proj, aes(x = X, y = Y, label = name), vjust = -1) +
  
  # Add a curved line from Dublin to Galway
  geom_curve(aes(x = -6.2603, y = 53.3498, xend = -9.0568, yend = 53.2707),
             curvature = 0.2, color = "purple", linewidth = 1, 
             arrow = arrow(length = unit(0.15, "inches"))) +
# Scale bar and compas
  annotation_scale(location = "bl", width_hint = 0.5, unit_category = "metric", 
                   bar_cols = c("darkblue", "white")) +
  annotation_north_arrow(location = "tl", which_north = "true", 
                         style = north_arrow_fancy_orienteering) +
  theme_minimal() +
  ggtitle("Map of Ireland (ITM Projection)")

# Add income levels
map2 <- ggplot2::ggplot() +
  geom_sf(data = ireland_proj, fill = "lightgreen", color = "darkblue") +
# County shapes
  geom_sf(data = counties_proj, fill = NA, color = "red", size = 0.5) +
# Median income
  geom_sf(data = counties_income_proj, aes(fill = mdn_income), color = "gold") +
  scale_fill_viridis_c(name = "Median Income (€)", na.value = "grey50") + 
# Cities
  geom_point(data = cities_proj, aes(x = X, y = Y), color = "red", size = 3) +
  geom_text(data = cities_proj, aes(x = X, y = Y, label = name), vjust = -1) +
  
  # Add a curved line from Dublin to Galway
  geom_curve(aes(x = -6.2603, y = 53.3498, xend = -9.0568, yend = 53.2707),
             curvature = 0.2, color = "purple", linewidth = 1, 
             arrow = arrow(length = unit(0.15, "inches"))) +
# Scale bar and compas
  annotation_scale(location = "bl", width_hint = 0.5, unit_category = "metric", 
                   bar_cols = c("darkblue", "white")) +
  annotation_north_arrow(location = "tl", which_north = "true", 
                         style = north_arrow_fancy_orienteering) +
  theme_minimal() +
  ggtitle("Median Income by County in Ireland (ITM Projection)")

map1

# Save as PNG
ggsave("ireland_map.png", plot = map1, width = 10, height = 8, dpi = 300)
# Save as PNG
ggsave("ireland_income_map.png", plot = map2, width = 10, height = 8, dpi = 300)

Interactive Layers

County Shapes + Cities + Income

# Create a color palette function
pal <- leaflet::colorNumeric(palette = "viridis", domain = counties_income$mdn_income)

# Convert cities to sf
cities_sf <- sf::st_as_sf(cities, coords = c("lon", "lat"), crs = 4326)

# Leaflet map with toggleable layers
leaflet::leaflet() %>%
  leaflet::addProviderTiles("CartoDB.Positron") %>%
  
  # County boundaries
  leaflet::addPolygons(
    data = countyshapes,
    color = "gold", weight = 1, fill = FALSE,
    group = "County Boundaries"
  ) %>%
  
  # Income overlay
  leaflet::addPolygons(
    data = counties_income,
    fillColor = ~pal(mdn_income),
    fillOpacity = 0.7,
    color = "darkblue",
    weight = 1,
    popup = ~paste(NAME_1, "<br>Median Income: €", mdn_income),
    group = "Median Income"
  ) %>%
  
  # Cities layer
  leaflet::addCircleMarkers(
    data = cities_sf,
    radius = 5,
    color = "red",
    label = ~name,
    group = "Cities"
  ) %>%
  
  # Legend
  leaflet::addLegend(
    pal = pal, values = counties_income$mdn_income,
    title = "Median Income (€)", opacity = 1
  ) %>%
  
  # Layers control
  leaflet::addLayersControl(
    overlayGroups = c("County Boundaries", "Median Income", "Cities"),
    options = layersControlOptions(collapsed = FALSE)
  )

County Shapes + Cities + Income + Route

# Define coordinates for Dublin and Galway
dublin <- c(-6.2603, 53.3498)
galway <- c(-9.0568, 53.2707)

# Create a great-circle intermediate line (curved line simulation)
route_line <- geosphere::gcIntermediate(dublin, galway, n = 100, addStartEnd = TRUE, sp = TRUE)

# Convert to sf object
route_sf <- sf::st_as_sf(route_line)

# Extend your existing leaflet map
leaflet::leaflet() %>%
  leaflet::addProviderTiles("CartoDB.Positron") %>%
  
  # County boundaries
  leaflet::addPolygons(
    data = countyshapes,
    color = "gold", weight = 1, fill = FALSE,
    group = "County Boundaries"
  ) %>%
  
  # Income overlay
  leaflet::addPolygons(
    data = counties_income,
    fillColor = ~pal(mdn_income),
    fillOpacity = 0.7,
    color = "darkblue",
    weight = 1,
    popup = ~paste(NAME_1, "<br>Median Income: €", mdn_income),
    group = "Median Income"
  ) %>%
  
  # Cities layer
  leaflet::addCircleMarkers(
    data = cities_sf,
    radius = 5,
    color = "red",
    label = ~name,
    group = "Cities"
  ) %>%
  
  # Route layer
  leaflet::addPolylines(
    data = route_sf,
    color = "purple",
    weight = 2,
    opacity = 0.8,
    group = "Route: Dublin to Galway"
  ) %>%
  
  # Legend
  leaflet::addLegend(
    pal = pal, values = counties_income$mdn_income,
    title = "Median Income (€)", opacity = 1
  ) %>%
  
  # Layers control
  leaflet::addLayersControl(
    overlayGroups = c("County Boundaries", "Median Income", "Cities", "Route: Dublin to Galway"),
    options = layersControlOptions(collapsed = FALSE)
  )

Animation

img1 <- magick::image_read("ireland_map.png")
img2 <- magick::image_read("ireland_income_map.png")

# Create an animated dissolve
magick::image_animate(magick::image_morph(c(img1, img2)),delay=20)